Safer releases
Detect regressions before they reach users with versioned evaluations and controlled rollout practices.
AI production and AgentOps service
Put AI into production with clear controls—and keep it dependable.
We close the gap between a working AI feature and a service your business can rely on. For agentic systems, managed Agent Operations—or AgentOps—means continuously evaluating behavior, tracing tool use, controlling change, responding to incidents, and tracking cost after release.
Business outcomes
What this work should improve.
Every engagement is tied to visible operational or business improvement—not technology activity alone.
Visible quality and cost
Connect traces, user outcomes, latency, model usage, and spend in operational dashboards.
Operational ownership
Define who owns model, prompt, data, tool permissions, product, and incident decisions.
What we deliver
The capabilities behind the outcome.
The exact scope is shaped around your estate, constraints, and team. These are the core capabilities we combine.
Production readiness review
Assess architecture, data flows, controls, evaluation, observability, resilience, and operating ownership.
Evaluation and release assurance
Build representative test sets, automated and human scoring, release gates, and regression checks.
Observability and response
Trace requests, retrieval, tool calls, latency, failures, quality, tokens, and cost; then connect them to alerts and runbooks.
Managed AgentOps
Operate versioning, approvals, policy controls, audit evidence, incident response, and continuous improvement.
Designed for
A clear fit before you commit.
Teams preparing an AI service for launch, operating agents without clear quality or cost measures, or strengthening governance and ownership around an existing production workload.
What you receive
Working capability, evidence, and a clear handover.
- Production-readiness assessment and prioritized gaps
- Versioned evaluation suite and release thresholds
- AI traces, dashboards, alerts, and cost visibility
- Governance, incident, fallback, and AgentOps runbooks
How we work
A practical path from evidence to improvement.
Each stage ends with a decision, working capability, or measured result. Your team stays involved throughout.
- 01
Review
Baseline the AI service against production, risk, and business requirements.
- 02
Instrument
Add tracing, metrics, evaluation, versioning, and cost visibility.
- 03
Control
Implement release gates, access controls, guardrails, fallback paths, and runbooks.
- 04
Improve
Use production evidence to manage regressions, drift, cost, and user outcomes.
A practical comparison
How our delivery model differs.
A focused point engagement can be the right choice. CloudVests is designed for work that must connect evidence, implementation, and operating responsibility.
| Area | CloudVests approach | Typical point engagement |
|---|---|---|
| Observability | End-to-end traces from input through retrieval, tools, model, and outcome | Infrastructure uptime and API error rates |
| Release confidence | Versioned task evaluations and controlled rollout | Manual prompt testing before release |
| Governance | Controls embedded in delivery and operations | Policy documents maintained separately |
| Optimization | Quality, latency, reliability, and cost managed together | Model cost optimized in isolation |
Evidence and expertise
Review the evidence behind our work.
Explore client outcomes, AWS credentials, and practical guidance before deciding what comes next.
Frequently asked questions
Questions teams ask before starting.
Have a question specific to your environment? We can review it with the right engineering specialist.
Ask CloudVestsWhat is AI production readiness?
It is evidence that an AI service meets agreed requirements for quality, safety, security, reliability, observability, cost, ownership, and recovery—not simply that the model returns a response.
What does AgentOps mean?
AgentOps is the operating discipline for AI agents in production. It covers evaluation, tracing, tool permissions, release control, incident response, human escalation, reliability, and cost so agent behavior remains visible and governed over time.
Can you assess an AI system another team built?
Yes. We can conduct an independent readiness review, prioritize gaps, implement selected improvements, or work alongside the existing product and engineering teams.
How do you test non-deterministic AI outputs?
We combine task-specific automated scoring, deterministic checks, model-based evaluation where appropriate, human review, statistical thresholds, and regression comparison across versions.
Can CloudVests operate the service after launch?
Yes. We can deliver a readiness engagement, enable your team, or provide managed AgentOps under an agreed scope covering evaluation, monitoring, response, reporting, and improvement.
Start with the real constraint
Tell us what needs to change.
Tell us about your priorities for AI production & AgentOps. We’ll bring the right specialists to define a practical next step.
Discuss AI production & AgentOps